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The middle value in a distribution.
Consists of editing, coding, data entry, and data cleaning.
Indicates how much the dependent variable changes for every one-unit increase in the independent variable.
The numerical difference between an observed value and the value predicted by the regression line.
Shows whether the association in a contingency table is statistically significant.
Replacing missing values in data analysis by estimating values from the available data.
Examples are Cramer’s phi and the correlation coefficient.
The most commonly used statistical measure of variation.
A cleaning technique that can be programmed for automatic detection in computer-assisted interviewing.
Documentation for a data file that usually contains the question wording and responses codes for each variable.
The value or category in a distribution with the highest frequency.
A graphic display of a univariate distribution.